New, previously unknown or unrecognized human pathogens are emerging faster than ever before. Furthermore, many existing human pathogens are evolving into new and potentially dangerous forms. At the same time, the world is becoming increasingly interconnected by air travel. Today more than 2.5 billion travelers board commercial flights every year, creating unprecedented opportunities for locally occurring infectious disease events to rapidly transform into international epidemics or global pandemics. Events such as the worldwide SARS outbreak in 2003 and the H1N1 pandemic in 2009 have clearly demonstrated the ease with which pathogens can spread across international borders and threaten human health, security, and economic activity.
Recent technological innovations to confront emerging global infectious diseases have focused on the early detection of potential threats through real-time analysis of massive volumes of Internet data. These innovations include software systems that analyze mass media content (e.g. online news), social media content (e.g. Twitter™), search engine activity (e.g. Google™ Flu trends), and other online communication channels for signs of potentially dangerous infectious diseases around the world. Recently, some of these systems have been coupled with information on global air traffic patterns to predict how a known human pathogen in a specific geography might spread around the world (see FIG. 1). These systems, which predict how individual infectious disease threats disseminate globally from a single geography at a defined moment in time, are incapable of forecasting or anticipating how a global array of infectious disease threats present risks to every geography in the world on a continuous real-time basis. There is therefore a need in the art for an improved method and system that is anticipatory in nature, which can forecast the local risks and consequences of continuously evolving global infectious disease activity.